Two-layer generalization boosting model for anomaly detection of e-commerce orders

WenLong Wang; HuanYi Li; TianYang Zhang; ZhaoKun Wang; YuTao Lai

doi:10.1117/12.3004538

11 October 2023 Two-layer generalization boosting model for anomaly detection of e-commerce orders

WenLong Wang, HuanYi Li, TianYang Zhang, ZhaoKun Wang, YuTao Lai

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128001Q (2023) https://doi.org/10.1117/12.3004538
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

As e-commerce continues to grow in popularity, the study of abnormal data in e-commerce orders is increasingly important. By analyzing abnormal data in e-commerce orders, one can uncover problems and trends within the order data, ultimately leading to improved operational efficiency. However, many abnormal detection models face issues such as overfitting, excessive dependence on sample size, and the neglect of other important features. To address these issues, this study proposes a novel approach that combines the ensemble model with MLP. Specifically, this approach leverages the strengths of Random Forest, GBDT, and XGBoost, which serve as the first-layer feature extractors. Samples that are misclassified in the first layer are discarded, and the remaining training samples are input into the second-layer MLP. This approach enhances the model's generalization ability, strengthens its global modeling capability to a certain extent, and mitigates the problem of excessive dependence on samples. The dataset from Gitee validates that this two-layer model can effectively extract useful information contained in the data, and its accuracy surpasses that of classical ensemble models.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

WenLong Wang, HuanYi Li, TianYang Zhang, ZhaoKun Wang, and YuTao Lai "Two-layer generalization boosting model for anomaly detection of e-commerce orders", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128001Q (11 October 2023); https://doi.org/10.1117/12.3004538

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